We compared the lesion localization ability of our method with the commonly used visualization techniques CAM [1]}, Grad-CAM [2]}, and the recent visual feature attribution using Wasserstein GANs \((\) VA-GAN\()\)  [3]}, and biomarker localization (BL) method [4]}. ResNet18 was chosen as the backbone of CAM and Grad-CAM to train a binary classification task. From Figure REF  (Left), it can be observed that CAM fails to detect most lesion areas and Grad-CAM detects the lesions but introduces many irrelevant area.
As for BL, it can indeed localize some lesions, but often fails to detect small or indistinct lesions. In comparison, our method provides much more precise localization (and therefore segmentation) of lesions even if the lesions are in irregular shapes or with vague boundaries, demonstrating the superior performance of our method to others.
This is also confirmed by the quantitative evaluation in the PR curve (Figure REF , Right), with the area under the PR curve (AUC) 0.63 for our approach, 0.36 for BL, 0.179 for Grad-CAM and 0.061 for CAM.
Based on the localization results, lesions can be automatically segmented by thresholding the heatmaps. Figure REF  shows that in a wide range of thresholds ([0.1, 0.4]), the simple threshold-based segmentation resulted in the dice score above 0.7, suggesting that the proposed framework can provide reasonably good segmentation even just based on the image-level labels.
